Cuadra AI - Connect, Train, and Deploy Your Custom AI Assistant

Available Models

Cuadra AI supports models from multiple leading AI providers. Use the catalog endpoint to discover what’s available:

curl https://api.cuadra.ai/v1/models/catalog \
  -H "Authorization: Bearer YOUR_TOKEN"

The catalog returns all available base models with their capabilities, context windows, and pricing. Use parentModelId from the catalog when creating your custom model.

Quick Start

First, get a parentModelId from the catalog, then create your custom model:

# 1. List available base models
curl https://api.cuadra.ai/v1/models/catalog \
  -H "Authorization: Bearer YOUR_TOKEN"

# 2. Create a model using a parent model ID
curl -X POST https://api.cuadra.ai/v1/models \
  -H "Authorization: Bearer YOUR_TOKEN" \
  -H "Content-Type: application/json" \
  -H "Idempotency-Key: create-model-001" \
  -d '{
    "parentModelId": "PARENT_MODEL_ID_FROM_CATALOG",
    "displayName": "Support Bot"
  }'

import httpx

# 1. Get parent model ID from catalog
catalog = httpx.get(
    "https://api.cuadra.ai/v1/models/catalog",
    headers={"Authorization": "Bearer YOUR_TOKEN"}
).json()
parent_id = catalog["items"][0]["id"]

# 2. Create your model
response = httpx.post(
    "https://api.cuadra.ai/v1/models",
    headers={"Authorization": "Bearer YOUR_TOKEN", "Idempotency-Key": "create-model-001"},
    json={"parentModelId": parent_id, "displayName": "Support Bot"}
)
model = response.json()
print(f"Created model: {model['id']}")

// 1. Get parent model ID from catalog
const catalogRes = await fetch('https://api.cuadra.ai/v1/models/catalog', {
  headers: { 'Authorization': 'Bearer YOUR_TOKEN' }
});
const catalog = await catalogRes.json();
const parentId = catalog.items[0].id;

// 2. Create your model
const response = await fetch('https://api.cuadra.ai/v1/models', {
  method: 'POST',
  headers: {
    'Authorization': 'Bearer YOUR_TOKEN',
    'Content-Type': 'application/json',
    'Idempotency-Key': 'create-model-001'
  },
  body: JSON.stringify({ 
    parentModelId: parentId, 
    displayName: 'Support Bot'
  })
});
const model = await response.json();
console.log(`Created model: ${model.id}`);

The parentModelId comes from GET /v1/models/catalog. These are pre-configured system models with standard pricing.

Linking Datasets (RAG)

Connect a knowledge base to enable retrieval-augmented generation:

curl -X POST https://api.cuadra.ai/v1/models/model_abc123/datasets \
  -H "Authorization: Bearer YOUR_TOKEN" \
  -H "Content-Type: application/json" \
  -d '{"datasetId": "ds_xyz789", "usageType": "rag"}'

import httpx

response = httpx.post(
    "https://api.cuadra.ai/v1/models/model_abc123/datasets",
    headers={"Authorization": "Bearer YOUR_TOKEN"},
    json={"datasetId": "ds_xyz789", "usageType": "rag"}
)
print("Dataset linked to model")

await fetch('https://api.cuadra.ai/v1/models/model_abc123/datasets', {
  method: 'POST',
  headers: {
    'Authorization': 'Bearer YOUR_TOKEN',
    'Content-Type': 'application/json'
  },
  body: JSON.stringify({ datasetId: 'ds_xyz789', usageType: 'rag' })
});
console.log('Dataset linked to model');

Usage Types

Usage Type	Behavior	Best For
`rag`	Search and retrieve relevant chunks per query	Large knowledge bases
`context`	Always include entire dataset in context	Small, always-relevant docs

Attaching System Prompts

Configure model behavior with a system prompt:

curl -X PATCH https://api.cuadra.ai/v1/models/model_abc123 \
  -H "Authorization: Bearer YOUR_TOKEN" \
  -H "Content-Type: application/json" \
  -d '{"systemPromptId": "sysprompt_xyz"}'

Use the Particles API to create modular, reusable prompt components.

FAQ

Can I switch LLM providers without changing code?

Yes. Update the model’s parentModelId via PATCH to point to a different base model. Your integration code using modelId stays the same.

What’s the difference between model and parentModelId?

Your model is a Cuadra AI model (custom displayName, linked datasets, system prompt). parentModelId references the underlying base model from the catalog.

How many datasets can I link?

No hard limit. However, more datasets = more RAG tokens = higher cost. Link only relevant datasets.

Do model changes affect existing conversations?

No. Conversations use the model configuration at creation time. Changes apply to new chats only.

Chat API

Use models in chat

System Prompts

Configure behavior

Getting Started

API Reference

Guides

Billing

Models API

Available Models

Quick Start

Linking Datasets (RAG)

Usage Types

Attaching System Prompts

FAQ

Can I switch LLM providers without changing code?

What’s the difference between model and parentModelId?

How many datasets can I link?

Do model changes affect existing conversations?

Chat API

System Prompts

​Available Models

​Quick Start

​Linking Datasets (RAG)

​Usage Types

​Attaching System Prompts

​FAQ

​Can I switch LLM providers without changing code?

​What’s the difference between model and parentModelId?

​How many datasets can I link?

​Do model changes affect existing conversations?

​Related

Chat API

System Prompts

Available Models

Quick Start

Linking Datasets (RAG)

Usage Types

Attaching System Prompts

FAQ

Can I switch LLM providers without changing code?

What’s the difference between model and parentModelId?

How many datasets can I link?

Do model changes affect existing conversations?

Related